UC Irvine

Studying communities impacted by traumatic events is often costly, requires swift action to enter the field when disaster strikes, and may be invasive for some traumatized respondents. Typically, individuals are studied after the traumatic event with no baseline data against which to compare their postdisaster responses. Given these challenges, we used longitudinal Twitter data across 3 case studies to examine the impact of violence near or on college campuses in the communities of Isla Vista, CA, Flagstaff, AZ, and Roseburg, OR, compared with control communities, between 2014 and 2015. To identify users likely to live in each community, we sought Twitter accounts local to those communities and downloaded tweets of their respective followers. Tweets were then coded for the presence of event-related negative emotion words using a computerized text analysis method (Linguistic Inquiry and Word Count, LIWC). In Case Study 1, we observed an increase in postevent negative emotion expression among sampled followers after mass violence, and show how patterns of response appear differently based on the timeframe under scrutiny. In Case Study 2, we replicate the pattern of results among users in the control group from Case Study 1 after a campus shooting in that community killed 1 student. In Case Study 3, we replicate this pattern in another group of Twitter users likely to live in a community affected by a mass shooting. We discuss conducting trauma-related research using Twitter data and provide guidance to researchers interested in using Twitter to answer their own research questions in this domain. (PsycINFO Database Record